A Neurodynamical Model of How Prior Knowledge Influences Visual Perception

Dražen Domijan, University of Rijeka

Mateja Marić, University of Rijeka

Abstract

Recent behavioral studies showed that prior knowledge can directly
influence visual perception. In the current work, we offer an explanation of the
observed findings based on the adaptive resonance theory (ART). The ART neural
network was designed to solve the problem of catastrophic forgetting during
learning in non-stationary environment. In the ART, stability of learning is
achieved by matching bottom-up sensory signals with top-down expectations.
Resonant state that corresponds with conscious perception develops in the network
when the bottom-up and top-down signals are closely aligned. On the other hand,
mismatch produces global reset signal that clears the traces of erroneous
top-down expectations. Therefore, prior knowledge can influence conscious
perception only when it already closely matches with sensory signals. We
performed computer simulations with real-time implementation of the ART circuit
that confirm our analysis. Simulations also showed how observed behavioral
findings arise from response bias.